Model-based run-time and memory reduction for a mixed-use multi-energy system model with high spatial resolution

Christian Klemm, Frauke Wiese and Peter Vennemann

Applied Energy, 2023, vol. 334, issue C, No S0306261922018311

Abstract: Local and regional energy systems are becoming increasingly entangled. Therefore, models for optimizing these energy systems are becoming more and more complex and the required computing resources (run-time and random access memory usage) are increasing rapidly. The computational requirements can basically be reduced solver-based (mathematical optimization of the solving process) or model-based (simplification of the real-world problem in the model). This paper deals with identifying how the required computational requirements for solving optimization models of multi-energy systems with high spatial resolution change with increasing model complexity and which model-based approaches enable to reduce the requirements with the lowest possible model deviations.

Keywords: Energy system model; Optimization; Model-based; Run-time; Memory usage; Modeling methods; Multi-energy system (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261922018311
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:334:y:2023:i:c:s0306261922018311

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2022.120574

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().